Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo MiMi: Sinhala Language Speech Assistive Learning Bot to Support Children with Stuttering(IEEE, 2022-12-13) Vithana, K.C.D; Weerarathne, D.N.N; Krishan, H.A.S; Wijesiri, M.R.M; Thelijjagoda, S; Jayawickrama, J. A. D. T.This research paper presents “MiMi”, a Sinhala Language voice assistive gamified solution that is designed to address stuttering in children aged between three and fourteen. Speech disorders occur when the regular flow of communication is disrupted. Stuttering, Lisps, Dysarthria, and Apraxia are some variations of speech impairments. Stuttering can be caused by a variety of factors including physical weaknesses, inherited diseases, Autism, and accidents. The risk of continuing to stutter into adulthood is highest in children between the ages of three to fourteen. It is recognized that stuttering therapy activities were less effective in managing stuttering after this age. Stuttering treatments comprise speech therapy with speech-language therapists, which requires in-person sessions that can be challenging and expensive in some circumstances. A parent’s financial ability, their busy schedules, the state of the economy in the nation, and the feasibility of physically seeing therapists and enduring treatments are all factors that might encourage or demotivate participation in therapy sessions. The development in technology and technical approaches have revolutionized the medical field and several studies have been conducted regarding communication disorders in recent years. The application can be used to practice a child’s needed speech therapy virtually and can also be used to aid speech therapy sessions done by speech therapists. The main aim of the system is to provide a customized, engaging, and innovative therapeutic strategy for children to manage stuttering.Publication Embargo DevFlair: A Framework to Automate the Pre-screening Process of Software Engineering Job Candidates(IEEE, 2022-12-09) Jayasekara, R.T.R; Kudarachchi, K.A.N.D; Kariyawasam, K.G.S.S.K; Rajapaksha, D; Jayasinghe, S.L; Thelijjagoda, SThe HR department of a technology company receives hundreds of job applications for each Software Engineering related vacancy. Evaluating a candidate by looking at the curriculum vitae may appear to be easy during the pre-screening process. However, an automated pre-screening process using Natural Language Processing and Machine Learning methodologies would help the recruiter to obtain a more accurate and deeper understanding of the candidate. In this paper we propose “DevFlair”, a framework to automate pre-screening Software Engineering job candidates. DevFlair uses data from social media, GitHub, and open-ended questionnaires to predict the Big-Five personality traits, analyze technical skill expertise, and analyze the experience in using industry-related online platforms. After analysis, the candidates are ranked according to their personality and technical skill levels. We conduct the personality prediction experiments using a social media posts dataset annotated with gold-standard Big-Five personality labels. We train FastText classification models and compare their accuracy against other state of the art classification models. The comparisons conclude that the FastText classification models substantially outperform the state of the art classification models when predicting Openness, Conscientiousness, and Agreeableness personality traits.Publication Embargo Assistant Zone – Homeschooling Assistance System based on Natural Language Processing(IEEE, 2022-12-09) Premendran, K; Bopearachchi, S.B.D.D.; Senevirathna, S.D.M.; Giridaran, S; Archchana, K; Ganegoda, D; Thelijjagoda, SAs a developing country, most people give their highest priority to education. When focusing on building an e-learning platform to improve the knowledge of students and teacher-student interactivity, the pandemic season can be mentioned as the main blocker which highly impacted the education field. Not only by considering the pandemic situation but also by addressing the concerns when it comes to teacher and student evaluation and psychological levels of students who are undergoing different difficulties, the “Home Schooling Assistance System” (Assistant Zone) has been introduced as a solution. The Assistant Zone has been initiated with three unique features which are valuable for both students and teachers. This system analyzes the strengths, weaknesses and evaluates the student performance, suggests study materials to improve themselves, provides solutions to the problems faced by the students, teachers, and parents and measures the performance of teachers based on their students, and recommends learning materials for the low-performing teachers. The Assistant Zone fulfills the targeted problems and introduces the above-mentioned three unique features with the use of Natural Language Processing (NLP) such as the BERT algorithm and Machine Learning models such as the Recurrent Neural Network, Forward Neural Network, and Gaussian Model.Publication Embargo Impact of Critical Success Factors in Oracle EBS Enterprise Resource Planning Post Go Live Implementation:A Case Study on SriLankan Airlines(Institute of Electrical and Electronics Engineers, 2022-01-08) Dissanayake, I; Thelijjagoda, SIn today's business world, ERP does everything from recording transaction data, managing workflows, analyzing data to provide insights to decision makers for effective decision making. Selection of a right ERP, proper testing and post go live could be a major scale system implementation for any organization. Thus, it is extremely beneficial to evaluate and test the Critical Success Factors (CSFs) in order to ensure a successful ERP post go live implementation. The primary goal of this study is to determine the impact of Critical Success Factors that influence for a successful post go live ERP implementation in the context of the national airline of Sri Lanka which is SriLankan Airlines. Three critical success factors were identified through this study. This study aims on Adequate end user training, Business Process Management (BPM) and Top Management Support as CSFs. The findings have verified that the ERP implementation success is influenced by the Top Management Support, Business Process Management and adequate end user trainings. Out of the identified three CSFs, it is statistically proven that the adequate end user training takes a significantly prominent place for a successful post go live implementation while BPM and Top Management Support also equally contribute to drive an ERP implementation project with expected outcomes. This study could be a guidance for enterprises, be beneficial to ERP clienteles, ERP consultants and service providers, be added to the existing body of knowledge.Publication Embargo AppGuider: Feature Comparison System using Neural Network with FastText and Aspect-based Sentiment Analysis on Play Store User Reviews(Institute of Electrical and Electronics Engineers, 2022-10-22) Thelijjagoda, S; Oshadi, D.M.KNowadays, there's a rapid growth in the number of apps downloaded from the app stores. People nowadays use apps for even the most simple daily tasks. In this situation, people always tend to search for new apps for the new tasks they come across in daily life. User reviews have a high impact on the app downloads. When analysing user reviews, it's important to consider the aspect that has been discussed in reviews. In mobile app reviews, the discussed aspect is mostly a functionality or feature of the mobile app. Therefore, it's crucial to make use of this important data in a way that helps app seekers to easily find the best-suited app for their requirements and also helps app developers to identify their weak features that need to be improved. This research was conducted to provide a strategy that visualizes user review summaries in a form that is relevant to the end user with the intention of achieving a model that is not only lightweight but also highly accurate and effective in terms of its performance. The AppGuider system was implemented, mainly with two models for sentiment analysis and aspect extraction. The sentiment classification model was developed with a deep learning approach that included a two-layer neural network, while the aspect extraction model was built with an unsupervised machine learning approach using the LdaMulticore algorithm. FastApi was used for data visualization in Frontend. User reviews were vectorized with FastText prior to input into the model. The accuracy of the sentiment classification model is 91%, with an 85.97% f1 score, an 85.93% recall, and an 86.05% precision. The FastText model outperformed the Stanford CoreNLP library in the performance test. The integrated system was evaluated by 25 user reviews that were entered manually and sentiment classification model scored 92% while the aspect extraction model scored of 76% accuracy.Publication Embargo AI Solution to Assist Online Education Productivity via Personalizing Learning Strategies and Analyzing the Student Performance(Institute of Electrical and Electronics Engineers, 2022-10-29) Liyanage, M.L.A.P.; Hirimuthugoda, U.J; Liyanage, N.L.T.N.; Thammita, D.H.M.M.P; Koliya Harshanath Webadu Wedanage, D; Kugathasan, A; Thelijjagoda, SHigher productivity in online education can be attained by consistent student engagement and appropriate use of learning resources and methodologies in the form of audio, video, and text. Lower literacy rates, decreased popularity, and unsatisfactory end-user goals can result from unbalanced or inappropriate use of the aforementioned. Prior studies mainly focused on identifying and separating the elements affecting the quality of online education and pinpointing the students' preferred learning styles outside of in-person and online instruction. This has not been able to clearly show how to enhance and customize the online learning environment in order to benefit the aforementioned criteria. This case study will primarily concentrate on elements that can be personalized and optimized to improve the quality of online education. With the aid of various algorithms like logistic regression,Support Vector Machines (SVM), time series forecasting (ARIMA), deep neural networks, and Recurrent Neural Networks (RNN), which make use of machine learning and deep learning techniques, the ultimate result has been attained. To increase application and accuracy, the newly presented technique will then be presented as a web-based software application. Contrary to what is commonly believed, this applied research proposes a new all-in-one Learning Management System (LMS) for students and tutors that acts as a central hub of all the learning resources.Publication Embargo Automated Spelling Checker And Grammatical Error Detection And Correction Model for Sinhala Language(IEEE, 2022-10-04) Goonawardena, M; Kulatunga, A; Wickramasinghe, R; Weerasekara, T; De Silva, H; Thelijjagoda, SSinhala is a native language spoken by the Sinhalese people, the largest ethnic group in Sri Lanka. It is a morphologically rich language, which is a derivation of Pali and Sanskrit. The Sinhala language creates a diglossia situation, as the language’s written form differs from its spoken form. With this difference, the written form requires more complex rules to be followed when in use. Manually proofreading the content of Sinhala material takes up much time and labor, and it can be a tedious task. Hence, a system is necessary which can be used by different industries such as journalism and even students. At present, there are a handful of systems and research that have automated Sinhala spelling analysis and grammar analysis. In addition, the existing systems are mainly focused on either spelling analysis or grammar analysis. However, the proposed system will cover both aspects and improve upon existing work by either optimizing or re-building the process to provide accurate outputs. The proposed system consists of a suffix list built for verbs and subjects, which helps the system stand out from the current proposed solutions. This research intends to implement a service for spell checking and grammar correctness of formal context in Sinhala. The research follows a rule-based approach with some components adopting a hybrid approach. As per the literature survey, many papers were analyzed, related to different aspects of the proposed system and complete systems. The proposed system would be able to overcome most barriers faced by previous papers whilst it takes a fresh take on providing a solution.Publication Embargo Healbot: NLP-based Health Care Assistant for Global Pandemics(IEEE, 2022-10-04) Anushka, S; Thelijjagoda, SSince it was detected, coronavirus (also known as COVID-19) has become a worldwide epidemic. The surge in patients has made it challenging for hospitals and medical professionals to keep up due to the increasing number of recorded incidents. When the pandemic starts, it is getting really hard to visit a medical specialist, even in more remote areas. According to the Johns Hopkins university’s Covid dashboard, approximately 220 million Covid cases were reported worldwide [2]. According to government hospital reports, 666,086 cases were found in Sri Lanka [3]. It’s a massive amount to handle for the health sector and the country. Consequently, there are many deaths reported every day as a result of the challenges in inpatient care. Because all patients are treated in their homes, this must be done efficiently. Only the most urgent cases are being treated in hospitals. Hospitals and quarantine centers are overcrowded. People in remote areas are also trying to treat the disease without knowing anything about it because they have limited access to information. This is because it needs a Chatbot to help with diagnosing Covid symptoms at home, and to assist patients in finding the right treatment options. An artificial intelligence (AI) Chatbot has been developed with the goal of diagnosing COVID-19 exposure and advising rapid remedies. As part of this analysis, relevant past research was reviewed to establish the best reliable approach for predicting COVID-19 in people. There was an integration of Logistic Regression, Decision Trees, and Random Forests to develop the model. The model was trained with the clinical data taken from the COVID-19 patients and machine learning models are evaluated to see how accurate they are. It was determined how accurate the algorithms were. The patients who were infected with COVID-19 were examined by using implemented prototype to predict the severity level and the trained model makes use of RASA Framework, FastAPI, and MongoDB for the pur...Publication Embargo Designing of a Voice-Based Programming IDE for Source Code Generation: A Machine Learning Approach(IEEE, 2022-10-04) Nizzad, A.R. M.; Thelijjagoda, SHumans are precise in recognizing natural languages and responding contextually unlike machines. However, speech recognition or Automatic speech recognition often refers to converting human speech or voice to textual information with the help of artificial intelligence algorithms. With the advancement of Artificial Intelligence technologies and extensive research being conducted in AI, speech recognition has received much attention and has emerged as a subset of Natural Language Processing where the advancement and accuracy in speech recognition will open many ways to provide a high standard of human-computer interaction. In this study, using the pre-trained transformer model with a transfer learning approach, the English to Python dataset was used to train the transformer model to produce syntactically correct source code in python. Additionally, the Word2Vec model was used to generate voice-to-text as input for the model. For the purpose of demonstration, a custom Python IDE is developed to generate source code from voice input. The results and findings suggest that in the transformer model, with the use of transfer learning, any dataset can be trained to produce syntactically correct source code. The model’s training loss and validation loss were below 5 and 2.1, respectively. Future research can focus on generating valid source code from any human spoken language without restricting it to English only.Publication Embargo Healthy Heart – Heart Risk Prediction System on Personalized Guidance for Heart Patients(IEEE, 2022-07-18) Bandara, K.R.C; Dureksha, D.D.T.D; Pinidiya, S.C; Amarasinghe, R.M.G.H; Thelijjagoda, S; Kishara, JHuman heart is the principal part of the human body. Change in human lifestyle, work related stress and unhealthy food habits contribute to the increase in rate of numerous heart related diseases. In accordance with several research, various heart diseases have been the key reason for deaths in Sri Lanka. According to the 2018 records, stroke affected 31%, coronary heart disease affected 23%, and ischemic heart disease affected 14%. Therefore, there is a need for an automated system which will enhance medical efficiency and to identify such diseases in time for proper treatment. The proposed system takes physical and medical datasets of heart patients as manual input parameters and predicts the patient’s risk of having a heart disease. Prediction process grants the patient a risk level according to the heart condition and proposes a personalized daily guidance for the patient to avoid risks associated with, along with a meal planner, exercise scheduler and a stress releaser as well as alert the patient well in advance. The system will present an efficient technique of predicting heart diseases using machine learning approaches to analyze huge complex medical data. Some of the used algorithms are Random Forest, Logistic regression, Decision tree classifier etc... The research mainly aims to prevent the escalation of heart diseases in patients and lead them to a healthy lifestyle.